In one embodiment, a computing device includes a computer-implemented system configured to generate a logical model item type in a self-describing data model implemented by the computer-implemented system, wherein the logical model item type defines a schema of an external object model implemented by a third-party computer-implemented system executed by a third-party computing device, the logical model item type comprises a logical model type (LMT) item type representing data of the third-party computer-implemented system. The computer-implemented system is configured to generate a mapping item type in the self-describing data model, wherein the mapping item type defines a mapping between an item type of the external object model and the LMT item type. The computer-implemented system is configured to use the mapping to enable data communication between the computer-implemented system and the third-party computer implemented system.

Pre-computed query results stored at a database server are re-computed by a computation machine. The pre-computed query results are composed of a plurality of shares. Each share includes a certain number of pre-computed query results. The computation resources of the computation machine needed to re-compute a pre-computed query result of a first share depend on whether or not other pre-computed query results of the first share are re-computed during a given time interval forming a current re-computation cycle. The computation machine receives a request to re-compute pre-computed query results of at least a first share, determines a number of pre-computed query results in the first share to be re-computed in the current re-computation cycle based on a Reinforcement Learning algorithm, and re-computes the determined number of pre-computed query results in the first share.

A method includes receiving a request to modify a first value of a first field of a first item in a self-describing data system, and obtaining a domain comprising items in the self-describing data system. The first item and a second item are included in items, and the second item comprises a second field having a second value. The method includes calculating, based on a rule of the second field, a dependency of the second value on the first value. The rule specifies how the second value is to be calculated using the first value. The method includes modifying, based on the request, the first value. The method includes receiving an event triggered by the modification to the first value. The method includes, responsive to the event, calculating the second value based on the rule, and storing the second value in the second field.

Systems and methods are described for unified processing of indexed and streaming data. A system enables users to query indexed data or specify processing pipelines to be applied to streaming data. In some instances, a user may specify a query intended to be run against indexed data, but may specify criteria that includes not-yet-indexed data (e.g., a future time frame). The system may convert the query into a data processing pipeline applied to not-yet-indexed data, thus increasing the efficiency of the system. Similarly, in some instances, a user may specify a data processing pipeline to be applied to a data stream, but specify criteria including data items outside the data stream. For example, a user may wish to apply the pipeline retroactively, to data items that have already exited the data stream. The system can convert the pipeline into a query against indexed data to satisfy the users processing requirements.

Systems, methods, and non-transitory computer readable media are provided for recursively searching a plurality of workspaces of the system for linked data associated with the seed data, initiating an endpoint process for each the seed data and the linked data, and, upon completion of the search, delete the seed data and the linked data identified based at least in part on the endpoint process. The process may be automatically repeated at a predetermined time interval to identify and remove future data that is stored in the plurality of datasets.

A method for performing recursive searching of items of a data structure having a data mode includes creating an instance of a query definition, the instance of the query definition comprising a unique identifier, specifying one or more elements of the query definition, providing the query definition as an input to a query engine. The method further includes the operations of determining, by the query engine, query execution instructions based on the query definition, the query instructions specifying a recursive level-by-level search until a terminal node of the data structure is reached, obtaining results of a query executed based on the query execution instructions; and outputting query results.

A computer-implemented method for processing a logic rule in a graph database. The method includes obtaining a graph database comprising at least one graph, each graph of the database being represented in one or more adjacency matrices (R-Matrix), each adjacency matrix representing a group of tuples of the graph comprising a same predicate, obtaining the logic rule concluding to a head predicate, generating a virtual adjacency matrix comprising one of the one or more adjacency matrices (R-Matrix) and an entailed data matrix (E-Matrix), the virtual adjacency matrix representing the head predicate, the entailed data matrix representing a group of tuples that are computed by applying the logic rule, and receiving a query by the database using the head predicate.

Systems and methods are configured to determine whether a particular information object is a duplicate of an object found in separate information objects. In various embodiments, the particular information object and each separate information object includes a set of data fields for storing data values that allows identical values to be stored in different fields for the objects. The data values for the particular information object are combined to form a data structure that includes a data element for each value. A determination as to whether the particular information object is an exact or partial match of a separate information object is made by performing a function on the data structure for the particular information object and a data structure for the separate information object to identify an intersection that includes data values for the particular information object that have an identical match with values for the separate information object.

Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises generating a context data tree for each variable in a plurality of variables based on a received input; determining data folding points for each generated context data tree; conducting a hyper-folding process on the determined data folding points in each context data tree, wherein the hyper-folding process converts each generated context data tree into a single data tree; and automatically loading the single data tree into an application.

This data aggregation/analysis system includes a user terminal and a database server. The user terminal comprises, a private key generation unit, an encrypted tabulated data generation unit which encrypts cells of tabulated data, an encrypted analysis query generation unit which generates an encrypted analysis query by encrypting item names of an analysis subject using a private key, and a transmission unit which transmits encrypted tabulated data, etc. The database server comprises: a storage unit which stores encrypted tabulated data, etc.; a tokenization unit which, upon reception of an encrypted analysis query, performs a search process using a searchable code matching function and receiving the encrypted analysis query and encrypted tabulated data as input, and tokenizes each found cell of encrypted tabulated data into a character string, thereby generating partially-tokenized encrypted tabulated data; a data analysis processing unit which receives the partially-tokenized encrypted tabulated data as input and generates a data analysis result; and a transmission unit which transmits the data analysis result to the user terminal.